Correlation between aggregated molecular cancer subtypes and selected clinical features

Ovarian Serous Cystadenocarcinoma (Primary solid tumor)

28 January 2016 | analyses__2016_01_28

Maintainer Information

Citation Information

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)

Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C15B01XK

Overview

Introduction

This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.

Summary

Testing the association between subtypes identified by 14 different clustering approaches and 7 clinical features across 589 patients, 16 significant findings detected with P value < 0.05 and Q value < 0.25.

CNMF clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'Time to Death' and 'YEARS_TO_BIRTH'.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'YEARS_TO_BIRTH'.
CNMF clustering analysis on array-based miR expression data identified 3 subtypes that correlate to 'Time to Death'.
Consensus hierarchical clustering analysis on array-based miR expression data identified 10 subtypes that correlate to 'YEARS_TO_BIRTH'.
3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH'.
4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH' and 'RADIATION_THERAPY'.
CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'Time to Death'.
Consensus hierarchical clustering analysis on RPPA data identified 4 subtypes that correlate to 'Time to Death' and 'YEARS_TO_BIRTH'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'YEARS_TO_BIRTH' and 'ETHNICITY'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'YEARS_TO_BIRTH'.
4 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'Time to Death'.
4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'YEARS_TO_BIRTH'.
4 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.
4 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes do not correlate to any clinical features.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 14 different clustering approaches and 7 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 16 significant findings detected.


Clinical Features	Time to Death	YEARS TO BIRTH	TUMOR TISSUE SITE	RADIATION THERAPY	KARNOFSKY PERFORMANCE SCORE	RESIDUAL TUMOR	ETHNICITY
Statistical Tests	logrank test	Kruskal-Wallis (anova)	Fisher's exact test	Fisher's exact test	Kruskal-Wallis (anova)	Fisher's exact test	Fisher's exact test
mRNA CNMF subtypes	0.0362 (0.232)	0.0104 (0.127)	0.388 (0.835)	1 (1.00)	0.385 (0.835)	0.258 (0.703)	0.315 (0.779)
mRNA cHierClus subtypes	0.147 (0.539)	0.0233 (0.19)	0.242 (0.698)	1 (1.00)	0.407 (0.84)	0.154 (0.539)	0.0635 (0.328)
miR CNMF subtypes	0.00157 (0.0257)	0.965 (1.00)	0.12 (0.539)	0.855 (1.00)	0.735 (1.00)	0.322 (0.779)	0.456 (0.844)
miR cHierClus subtypes	0.188 (0.613)	0.000445 (0.0109)	0.804 (1.00)	0.249 (0.698)	0.903 (1.00)	0.266 (0.705)	0.437 (0.84)
Copy Number Ratio CNMF subtypes	0.69 (1.00)	1.16e-09 (5.71e-08)	0.392 (0.835)	0.135 (0.539)	0.334 (0.78)	0.485 (0.849)	0.774 (1.00)
METHLYATION CNMF	0.139 (0.539)	4.84e-12 (4.75e-10)	0.411 (0.84)	0.012 (0.13)	0.246 (0.698)	0.852 (1.00)	0.654 (1.00)
RPPA CNMF subtypes	0.0268 (0.202)	0.453 (0.844)	1 (1.00)	0.532 (0.869)	0.481 (0.849)	0.314 (0.779)	0.723 (1.00)
RPPA cHierClus subtypes	0.00323 (0.0452)	0.0379 (0.232)	0.0526 (0.303)	0.904 (1.00)	0.938 (1.00)	0.518 (0.86)	0.117 (0.539)
RNAseq CNMF subtypes	0.791 (1.00)	0.000846 (0.0166)	0.784 (1.00)	0.199 (0.625)	0.427 (0.84)	0.51 (0.86)	0.0183 (0.163)
RNAseq cHierClus subtypes	0.124 (0.539)	0.0145 (0.142)	0.59 (0.932)	0.467 (0.847)	0.204 (0.625)	0.589 (0.932)	0.326 (0.779)
MIRSEQ CNMF	0.0353 (0.232)	0.911 (1.00)	0.0579 (0.315)	1 (1.00)	0.98 (1.00)		0.631 (0.982)
MIRSEQ CHIERARCHICAL	0.15 (0.539)	0.000116 (0.00379)	0.869 (1.00)	0.514 (0.86)	0.677 (1.00)		0.773 (1.00)
MIRseq Mature CNMF subtypes	0.183 (0.613)	0.374 (0.835)	1 (1.00)
MIRseq Mature cHierClus subtypes	0.136 (0.539)	0.434 (0.84)	1 (1.00)

Clustering Approach #1: 'mRNA CNMF subtypes'

Table S1. Description of clustering approach #1: 'mRNA CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	181	113	120	149

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.0362 (logrank test), Q value = 0.23

Table S2. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	559	334	0.3 - 180.2 (33.1)
subtype1	180	108	0.3 - 152.0 (33.1)
subtype2	113	77	0.3 - 153.4 (31.0)
subtype3	120	83	1.2 - 123.2 (37.8)
subtype4	146	66	0.4 - 180.2 (32.2)

Figure S1. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0104 (Kruskal-Wallis (anova)), Q value = 0.13

Table S3. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	552	59.7 (11.6)
subtype1	175	61.9 (11.7)
subtype2	112	60.3 (11.9)
subtype3	119	58.3 (11.1)
subtype4	146	57.9 (11.4)

Figure S2. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'mRNA CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.388 (Fisher's exact test), Q value = 0.84

Table S4. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	OMENTUM	OVARY	PERITONEUM OVARY
ALL	2	559	2
subtype1	0	181	0
subtype2	1	111	1
subtype3	0	120	0
subtype4	1	147	1

Figure S3. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'mRNA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 1 (Fisher's exact test), Q value = 1

Table S5. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	541	5
subtype1	173	2
subtype2	108	1
subtype3	118	1
subtype4	142	1

Figure S4. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

'mRNA CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.385 (Kruskal-Wallis (anova)), Q value = 0.84

Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	78	75.6 (12.8)
subtype1	21	75.2 (14.0)
subtype2	16	71.2 (12.6)
subtype3	24	76.7 (12.7)
subtype4	17	78.8 (11.1)

Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'mRNA CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.258 (Fisher's exact test), Q value = 0.7

Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2
ALL	14	27	1
subtype1	7	7	0
subtype2	1	8	1
subtype3	1	3	0
subtype4	5	9	0

Figure S6. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

'mRNA CNMF subtypes' versus 'ETHNICITY'

P value = 0.315 (Fisher's exact test), Q value = 0.78

Table S8. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	11	330
subtype1	5	109
subtype2	0	69
subtype3	2	73
subtype4	4	79

Figure S7. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S9. Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	313	161	89

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.147 (logrank test), Q value = 0.54

Table S10. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	559	334	0.3 - 180.2 (33.1)
subtype1	309	174	0.3 - 153.4 (33.9)
subtype2	161	101	0.3 - 180.2 (30.1)
subtype3	89	59	0.8 - 152.0 (38.4)

Figure S8. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0233 (Kruskal-Wallis (anova)), Q value = 0.19

Table S11. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	552	59.7 (11.6)
subtype1	307	59.5 (11.4)
subtype2	158	58.4 (12.0)
subtype3	87	62.9 (11.3)

Figure S9. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'mRNA cHierClus subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.242 (Fisher's exact test), Q value = 0.7

Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	OMENTUM	OVARY	PERITONEUM OVARY
ALL	2	559	2
subtype1	1	312	0
subtype2	1	158	2
subtype3	0	89	0

Figure S10. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'mRNA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 1 (Fisher's exact test), Q value = 1

Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	541	5
subtype1	301	3
subtype2	154	1
subtype3	86	1

Figure S11. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

'mRNA cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.407 (Kruskal-Wallis (anova)), Q value = 0.84

Table S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	78	75.6 (12.8)
subtype1	43	76.7 (11.5)
subtype2	21	72.4 (13.4)
subtype3	14	77.1 (15.4)

Figure S12. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'mRNA cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.154 (Fisher's exact test), Q value = 0.54

Table S15. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2
ALL	14	27	1
subtype1	8	11	0
subtype2	2	12	1
subtype3	4	4	0

Figure S13. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

'mRNA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.0635 (Fisher's exact test), Q value = 0.33

Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	11	330
subtype1	8	179
subtype2	0	94
subtype3	3	57

Figure S14. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #3: 'miR CNMF subtypes'

Table S17. Description of clustering approach #3: 'miR CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	181	133	247

'miR CNMF subtypes' versus 'Time to Death'

P value = 0.00157 (logrank test), Q value = 0.026

Table S18. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	557	334	0.3 - 180.2 (33.1)
subtype1	181	111	0.3 - 153.4 (32.1)
subtype2	129	85	0.3 - 145.4 (29.1)
subtype3	247	138	0.3 - 180.2 (36.3)

Figure S15. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'miR CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.965 (Kruskal-Wallis (anova)), Q value = 1

Table S19. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	550	59.7 (11.6)
subtype1	177	59.6 (12.3)
subtype2	131	59.4 (11.6)
subtype3	242	60.0 (11.1)

Figure S16. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'miR CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.12 (Fisher's exact test), Q value = 0.54

Table S20. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	OMENTUM	OVARY	PERITONEUM OVARY
ALL	2	557	2
subtype1	1	178	2
subtype2	1	132	0
subtype3	0	247	0

Figure S17. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'miR CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.855 (Fisher's exact test), Q value = 1

Table S21. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	539	5
subtype1	173	1
subtype2	127	1
subtype3	239	3

Figure S18. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

'miR CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.735 (Kruskal-Wallis (anova)), Q value = 1

Table S22. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	78	75.6 (12.8)
subtype1	25	76.8 (12.5)
subtype2	17	74.1 (15.4)
subtype3	36	75.6 (11.8)

Figure S19. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'miR CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.322 (Fisher's exact test), Q value = 0.78

Table S23. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2
ALL	14	27	1
subtype1	5	12	0
subtype2	2	7	1
subtype3	7	8	0

Figure S20. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

'miR CNMF subtypes' versus 'ETHNICITY'

P value = 0.456 (Fisher's exact test), Q value = 0.84

Table S24. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #7: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	11	329
subtype1	5	101
subtype2	1	79
subtype3	5	149

Figure S21. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #4: 'miR cHierClus subtypes'

Table S25. Description of clustering approach #4: 'miR cHierClus subtypes'

Cluster Labels	1	10	2	3	4	5	6	7	8	9
Number of samples	83	57	58	86	35	75	36	36	51	44

'miR cHierClus subtypes' versus 'Time to Death'

P value = 0.188 (logrank test), Q value = 0.61

Table S26. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	557	334	0.3 - 180.2 (33.1)
subtype1	83	52	0.3 - 153.4 (31.2)
subtype10	56	30	2.0 - 115.9 (28.2)
subtype2	58	37	0.3 - 106.0 (28.3)
subtype3	86	48	0.3 - 127.3 (36.8)
subtype4	34	26	1.2 - 134.0 (33.6)
subtype5	74	43	1.7 - 180.2 (38.6)
subtype6	36	25	1.0 - 125.6 (35.8)
subtype7	36	16	6.1 - 124.4 (31.7)
subtype8	50	28	0.5 - 152.0 (26.7)
subtype9	44	29	1.0 - 127.8 (32.2)

Figure S22. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'miR cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.000445 (Kruskal-Wallis (anova)), Q value = 0.011

Table S27. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	550	59.7 (11.6)
subtype1	80	58.7 (12.2)
subtype10	56	55.6 (11.2)
subtype2	58	61.7 (12.3)
subtype3	85	59.3 (11.2)
subtype4	35	60.3 (11.7)
subtype5	74	60.0 (10.0)
subtype6	35	63.3 (10.9)
subtype7	35	62.9 (12.0)
subtype8	49	63.6 (11.4)
subtype9	43	54.3 (11.3)

Figure S23. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'miR cHierClus subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.804 (Fisher's exact test), Q value = 1

Table S28. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	OMENTUM	OVARY	PERITONEUM OVARY
ALL	2	557	2
subtype1	0	82	1
subtype10	0	57	0
subtype2	0	58	0
subtype3	1	85	0
subtype4	0	34	1
subtype5	1	74	0
subtype6	0	36	0
subtype7	0	36	0
subtype8	0	51	0
subtype9	0	44	0

Figure S24. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'miR cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.249 (Fisher's exact test), Q value = 0.7

Table S29. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	539	5
subtype1	77	1
subtype10	55	0
subtype2	56	1
subtype3	85	0
subtype4	34	0
subtype5	74	0
subtype6	35	0
subtype7	33	1
subtype8	48	2
subtype9	42	0

Figure S25. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

'miR cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.903 (Kruskal-Wallis (anova)), Q value = 1

Table S30. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	78	75.6 (12.8)
subtype1	13	73.8 (15.0)
subtype10	6	73.3 (16.3)
subtype2	4	75.0 (10.0)
subtype3	13	80.0 (11.5)
subtype4	3	73.3 (11.5)
subtype5	12	76.7 (16.7)
subtype6	7	77.1 (13.8)
subtype7	6	76.7 (8.2)
subtype8	5	68.0 (11.0)
subtype9	9	75.6 (8.8)

Figure S26. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'miR cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.266 (Fisher's exact test), Q value = 0.7

Table S31. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2
ALL	14	27	1
subtype1	2	7	0
subtype10	2	1	0
subtype2	1	2	0
subtype3	2	4	0
subtype4	0	5	0
subtype5	1	6	0
subtype6	1	1	0
subtype7	2	1	0
subtype8	2	0	0
subtype9	1	0	1

Figure S27. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

'miR cHierClus subtypes' versus 'ETHNICITY'

P value = 0.437 (Fisher's exact test), Q value = 0.84

Table S32. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #7: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	11	329
subtype1	3	46
subtype10	0	36
subtype2	0	25
subtype3	3	56
subtype4	1	18
subtype5	1	44
subtype6	2	23
subtype7	1	19
subtype8	0	34
subtype9	0	28

Figure S28. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #5: 'Copy Number Ratio CNMF subtypes'

Table S33. Description of clustering approach #5: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	241	129	203

'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.69 (logrank test), Q value = 1

Table S34. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	559	331	0.3 - 180.2 (32.9)
subtype1	235	138	0.3 - 180.2 (29.1)
subtype2	126	80	0.8 - 153.4 (33.6)
subtype3	198	113	0.3 - 134.0 (34.9)

Figure S29. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'Copy Number Ratio CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 1.16e-09 (Kruskal-Wallis (anova)), Q value = 5.7e-08

Table S35. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	552	59.8 (11.6)
subtype1	231	63.4 (10.7)
subtype2	123	58.6 (12.5)
subtype3	198	56.5 (10.8)

Figure S30. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'Copy Number Ratio CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.392 (Fisher's exact test), Q value = 0.84

Table S36. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	OMENTUM	OVARY	PERITONEUM OVARY
ALL	3	558	2
subtype1	2	233	2
subtype2	1	126	0
subtype3	0	199	0

Figure S31. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.135 (Fisher's exact test), Q value = 0.54

Table S37. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	541	5
subtype1	229	1
subtype2	118	3
subtype3	194	1

Figure S32. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.334 (Kruskal-Wallis (anova)), Q value = 0.78

Table S38. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	82	75.7 (13.9)
subtype1	38	76.1 (15.7)
subtype2	14	80.0 (11.1)
subtype3	30	73.3 (12.4)

Figure S33. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'Copy Number Ratio CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.485 (Fisher's exact test), Q value = 0.85

Table S39. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	15	26	5	3
subtype1	10	12	3	2
subtype2	2	9	0	0
subtype3	3	5	2	1

Figure S34. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.774 (Fisher's exact test), Q value = 1

Table S40. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	10	327
subtype1	5	139
subtype2	1	74
subtype3	4	114

Figure S35. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #6: 'METHLYATION CNMF'

Table S41. Description of clustering approach #6: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4
Number of samples	118	100	188	169

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.139 (logrank test), Q value = 0.54

Table S42. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	560	336	0.3 - 180.2 (33.2)
subtype1	116	71	0.3 - 153.4 (31.0)
subtype2	95	61	0.8 - 116.7 (29.1)
subtype3	184	107	0.3 - 180.2 (34.3)
subtype4	165	97	0.8 - 152.0 (35.0)

Figure S36. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 4.84e-12 (Kruskal-Wallis (anova)), Q value = 4.7e-10

Table S43. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	554	59.8 (11.6)
subtype1	116	62.9 (12.2)
subtype2	95	62.0 (12.2)
subtype3	182	54.4 (10.2)
subtype4	161	62.3 (10.2)

Figure S37. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'METHLYATION CNMF' versus 'TUMOR_TISSUE_SITE'

P value = 0.411 (Fisher's exact test), Q value = 0.84

Table S44. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	OMENTUM	OVARY	PERITONEUM OVARY
ALL	2	561	2
subtype1	1	117	0
subtype2	0	97	0
subtype3	1	184	0
subtype4	0	163	2

Figure S38. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 0.012 (Fisher's exact test), Q value = 0.13

Table S45. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	542	5
subtype1	111	3
subtype2	90	2
subtype3	180	0
subtype4	161	0

Figure S39. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATION_THERAPY'

'METHLYATION CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.246 (Kruskal-Wallis (anova)), Q value = 0.7

Table S46. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	78	75.6 (12.8)
subtype1	21	73.3 (13.2)
subtype2	15	73.3 (14.5)
subtype3	21	80.0 (8.9)
subtype4	21	75.2 (14.0)

Figure S40. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'METHLYATION CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.852 (Fisher's exact test), Q value = 1

Table S47. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2
ALL	14	26	2
subtype1	3	5	0
subtype2	2	8	0
subtype3	5	6	1
subtype4	4	7	1

Figure S41. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.654 (Fisher's exact test), Q value = 1

Table S48. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #7: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	11	331
subtype1	2	80
subtype2	1	57
subtype3	3	104
subtype4	5	90

Figure S42. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #7: 'RPPA CNMF subtypes'

Table S49. Description of clustering approach #7: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	150	192	78

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0268 (logrank test), Q value = 0.2

Table S50. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	416	247	0.3 - 180.2 (33.6)
subtype1	147	94	0.4 - 153.4 (31.6)
subtype2	191	103	0.3 - 180.2 (35.8)
subtype3	78	50	1.1 - 91.7 (28.7)

Figure S43. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.453 (Kruskal-Wallis (anova)), Q value = 0.84

Table S51. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	413	59.7 (11.8)
subtype1	148	59.9 (12.0)
subtype2	189	59.1 (11.6)
subtype3	76	60.8 (11.9)

Figure S44. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 1 (Fisher's exact test), Q value = 1

Table S52. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	OMENTUM	OVARY	PERITONEUM OVARY
ALL	3	415	2
subtype1	1	148	1
subtype2	2	189	1
subtype3	0	78	0

Figure S45. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.532 (Fisher's exact test), Q value = 0.87

Table S53. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	401	4
subtype1	141	2
subtype2	186	1
subtype3	74	1

Figure S46. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

'RPPA CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.481 (Kruskal-Wallis (anova)), Q value = 0.85

Table S54. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	57	75.3 (13.5)
subtype1	25	76.8 (12.8)
subtype2	27	73.0 (14.1)
subtype3	5	80.0 (14.1)

Figure S47. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.314 (Fisher's exact test), Q value = 0.78

Table S55. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	14	29	5	3
subtype1	2	12	1	1
subtype2	9	8	3	1
subtype3	3	9	1	1

Figure S48. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.723 (Fisher's exact test), Q value = 1

Table S56. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	215
subtype1	2	79
subtype2	5	98
subtype3	1	38

Figure S49. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #8: 'RPPA cHierClus subtypes'

Table S57. Description of clustering approach #8: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	146	57	61	156

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.00323 (logrank test), Q value = 0.045

Table S58. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	416	247	0.3 - 180.2 (33.6)
subtype1	145	89	0.4 - 180.2 (30.2)
subtype2	57	42	0.8 - 90.4 (34.8)
subtype3	61	31	0.8 - 127.8 (36.9)
subtype4	153	85	0.3 - 152.0 (35.0)

Figure S50. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0379 (Kruskal-Wallis (anova)), Q value = 0.23

Table S59. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	413	59.7 (11.8)
subtype1	144	59.9 (11.9)
subtype2	57	62.8 (10.7)
subtype3	61	57.0 (12.4)
subtype4	151	59.4 (11.6)

Figure S51. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.0526 (Fisher's exact test), Q value = 0.3

Table S60. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	OMENTUM	OVARY	PERITONEUM OVARY
ALL	3	415	2
subtype1	1	145	0
subtype2	2	54	1
subtype3	0	61	0
subtype4	0	155	1

Figure S52. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.904 (Fisher's exact test), Q value = 1

Table S61. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	401	4
subtype1	140	1
subtype2	56	0
subtype3	59	1
subtype4	146	2

Figure S53. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

'RPPA cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.938 (Kruskal-Wallis (anova)), Q value = 1

Table S62. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	57	75.3 (13.5)
subtype1	14	76.4 (16.5)
subtype2	9	73.3 (17.3)
subtype3	17	74.1 (11.8)
subtype4	17	76.5 (11.1)

Figure S54. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.518 (Fisher's exact test), Q value = 0.86

Table S63. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	14	29	5	3
subtype1	4	13	3	0
subtype2	3	6	0	1
subtype3	2	5	0	0
subtype4	5	5	2	2

Figure S55. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.117 (Fisher's exact test), Q value = 0.54

Table S64. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	215
subtype1	1	75
subtype2	2	25
subtype3	3	32
subtype4	2	83

Figure S56. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #9: 'RNAseq CNMF subtypes'

Table S65. Description of clustering approach #9: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	79	110	114

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.791 (logrank test), Q value = 1

Table S66. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	302	183	0.3 - 180.2 (31.3)
subtype1	78	44	0.8 - 116.1 (34.6)
subtype2	110	69	0.3 - 152.0 (30.6)
subtype3	114	70	0.3 - 180.2 (30.1)

Figure S57. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.000846 (Kruskal-Wallis (anova)), Q value = 0.017

Table S67. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	295	59.1 (10.9)
subtype1	76	55.4 (10.8)
subtype2	107	61.3 (10.5)
subtype3	112	59.6 (10.8)

Figure S58. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.784 (Fisher's exact test), Q value = 1

Table S68. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	OMENTUM	OVARY
ALL	3	300
subtype1	0	79
subtype2	1	109
subtype3	2	112

Figure S59. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.199 (Fisher's exact test), Q value = 0.62

Table S69. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	288	2
subtype1	75	0
subtype2	104	2
subtype3	109	0

Figure S60. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

'RNAseq CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.427 (Kruskal-Wallis (anova)), Q value = 0.84

Table S70. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	20	78.0 (16.1)
subtype1	4	72.5 (22.2)
subtype2	6	85.0 (12.2)
subtype3	10	76.0 (15.8)

Figure S61. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.51 (Fisher's exact test), Q value = 0.86

Table S71. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	3	5	2	3
subtype1	1	0	1	1
subtype2	2	2	1	2
subtype3	0	3	0	0

Figure S62. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.0183 (Fisher's exact test), Q value = 0.16

Table S72. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	3	163
subtype1	3	41
subtype2	0	60
subtype3	0	62

Figure S63. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #10: 'RNAseq cHierClus subtypes'

Table S73. Description of clustering approach #10: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	147	60	96

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.124 (logrank test), Q value = 0.54

Table S74. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	302	183	0.3 - 180.2 (31.3)
subtype1	146	83	0.3 - 180.2 (35.5)
subtype2	60	40	1.0 - 152.0 (26.4)
subtype3	96	60	0.3 - 145.4 (30.1)

Figure S64. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0145 (Kruskal-Wallis (anova)), Q value = 0.14

Table S75. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	295	59.1 (10.9)
subtype1	142	57.4 (10.6)
subtype2	59	62.4 (10.8)
subtype3	94	59.7 (11.0)

Figure S65. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.59 (Fisher's exact test), Q value = 0.93

Table S76. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	OMENTUM	OVARY
ALL	3	300
subtype1	1	146
subtype2	0	60
subtype3	2	94

Figure S66. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.467 (Fisher's exact test), Q value = 0.85

Table S77. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	288	2
subtype1	138	1
subtype2	58	1
subtype3	92	0

Figure S67. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

'RNAseq cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.204 (Kruskal-Wallis (anova)), Q value = 0.62

Table S78. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	20	78.0 (16.1)
subtype1	9	75.6 (14.2)
subtype2	3	93.3 (11.5)
subtype3	8	75.0 (17.7)

Figure S68. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.589 (Fisher's exact test), Q value = 0.93

Table S79. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	3	5	2	3
subtype1	1	3	1	2
subtype2	2	0	1	1
subtype3	0	2	0	0

Figure S69. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RESIDUAL_TUMOR'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.326 (Fisher's exact test), Q value = 0.78

Table S80. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	3	163
subtype1	3	73
subtype2	0	39
subtype3	0	51

Figure S70. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #11: 'MIRSEQ CNMF'

Table S81. Description of clustering approach #11: 'MIRSEQ CNMF'

Cluster Labels	1	2	3	4
Number of samples	74	135	141	103

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0353 (logrank test), Q value = 0.23

Table S82. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	451	283	0.3 - 180.2 (35.0)
subtype1	74	51	0.3 - 152.0 (30.1)
subtype2	134	94	1.0 - 153.4 (43.7)
subtype3	140	82	0.3 - 130.0 (29.8)
subtype4	103	56	0.3 - 180.2 (37.0)

Figure S71. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.911 (Kruskal-Wallis (anova)), Q value = 1

Table S83. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	445	59.8 (11.5)
subtype1	73	60.5 (12.2)
subtype2	134	59.8 (11.4)
subtype3	137	59.4 (12.0)
subtype4	101	59.8 (10.5)

Figure S72. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'TUMOR_TISSUE_SITE'

P value = 0.0579 (Fisher's exact test), Q value = 0.32

Table S84. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	OMENTUM	OVARY	PERITONEUM OVARY
ALL	2	450	1
subtype1	1	72	1
subtype2	0	135	0
subtype3	0	141	0
subtype4	1	102	0

Figure S73. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 1 (Fisher's exact test), Q value = 1

Table S85. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	438	3
subtype1	72	0
subtype2	132	1
subtype3	135	1
subtype4	99	1

Figure S74. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #4: 'RADIATION_THERAPY'

'MIRSEQ CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.98 (Kruskal-Wallis (anova)), Q value = 1

Table S86. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	64	75.3 (13.2)
subtype1	6	76.7 (15.1)
subtype2	38	75.8 (13.3)
subtype3	8	72.5 (14.9)
subtype4	12	75.0 (12.4)

Figure S75. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.631 (Fisher's exact test), Q value = 0.98

Table S87. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #7: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	263
subtype1	0	44
subtype2	4	89
subtype3	2	79
subtype4	2	51

Figure S76. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #12: 'MIRSEQ CHIERARCHICAL'

Table S88. Description of clustering approach #12: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3	4
Number of samples	105	73	169	106

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.15 (logrank test), Q value = 0.54

Table S89. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	451	283	0.3 - 180.2 (35.0)
subtype1	105	63	0.3 - 153.4 (43.9)
subtype2	73	48	0.8 - 119.0 (29.2)
subtype3	168	113	0.3 - 180.2 (34.8)
subtype4	105	59	0.3 - 130.0 (32.5)

Figure S77. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.000116 (Kruskal-Wallis (anova)), Q value = 0.0038

Table S90. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	445	59.8 (11.5)
subtype1	104	56.9 (11.5)
subtype2	73	62.5 (11.8)
subtype3	166	61.8 (11.1)
subtype4	102	57.4 (10.9)

Figure S78. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'TUMOR_TISSUE_SITE'

P value = 0.869 (Fisher's exact test), Q value = 1

Table S91. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	OMENTUM	OVARY	PERITONEUM OVARY
ALL	2	450	1
subtype1	0	105	0
subtype2	0	73	0
subtype3	2	166	1
subtype4	0	106	0

Figure S79. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.514 (Fisher's exact test), Q value = 0.86

Table S92. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	438	3
subtype1	103	0
subtype2	70	1
subtype3	162	2
subtype4	103	0

Figure S80. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'RADIATION_THERAPY'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.677 (Kruskal-Wallis (anova)), Q value = 1

Table S93. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	64	75.3 (13.2)
subtype1	28	76.4 (11.0)
subtype2	6	70.0 (21.0)
subtype3	26	74.6 (14.5)
subtype4	4	80.0 (0.0)

Figure S81. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.773 (Fisher's exact test), Q value = 1

Table S94. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	263
subtype1	3	70
subtype2	2	45
subtype3	2	91
subtype4	1	57

Figure S82. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #13: 'MIRseq Mature CNMF subtypes'

Table S95. Description of clustering approach #13: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	6	6	5	5

'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.183 (logrank test), Q value = 0.61

Table S96. Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	22	15	0.8 - 125.8 (30.4)
subtype1	6	5	15.8 - 55.9 (28.4)
subtype2	6	2	5.4 - 115.9 (64.8)
subtype3	5	5	11.1 - 67.4 (29.0)
subtype4	5	3	0.8 - 125.8 (4.8)

Figure S83. Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.374 (Kruskal-Wallis (anova)), Q value = 0.84

Table S97. Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	21	60.5 (12.6)
subtype1	6	56.0 (12.9)
subtype2	5	56.0 (8.3)
subtype3	5	64.2 (17.0)
subtype4	5	66.6 (10.5)

Figure S84. Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 1 (Fisher's exact test), Q value = 1

Table S98. Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	OMENTUM	OVARY
ALL	1	21
subtype1	1	5
subtype2	0	6
subtype3	0	5
subtype4	0	5

Figure S85. Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

Clustering Approach #14: 'MIRseq Mature cHierClus subtypes'

Table S99. Description of clustering approach #14: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6
Number of samples	5	2	2	4	5	4

'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.136 (logrank test), Q value = 0.54

Table S100. Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	18	12	0.8 - 125.8 (30.4)
subtype1	5	4	15.8 - 55.9 (29.0)
subtype4	4	1	5.4 - 115.9 (64.8)
subtype5	5	4	0.8 - 125.8 (36.2)
subtype6	4	3	1.0 - 42.0 (12.7)

Figure S86. Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.434 (Kruskal-Wallis (anova)), Q value = 0.84

Table S101. Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	17	59.4 (13.1)
subtype1	5	55.6 (14.9)
subtype4	3	52.3 (8.7)
subtype5	5	66.4 (8.1)
subtype6	4	60.5 (17.9)

Figure S87. Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 1 (Fisher's exact test), Q value = 1

Table S102. Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	OMENTUM	OVARY
ALL	1	17
subtype1	0	5
subtype4	0	4
subtype5	1	4
subtype6	0	4

Figure S88. Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

Methods & Data

Input

Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/OV-TP/22555032/OV-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/OV-TP/22507289/OV-TP.merged_data.txt
Number of patients = 589
Number of clustering approaches = 14
Number of selected clinical features = 7
Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches

CNMF clustering

consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)

Consensus hierarchical clustering

Resampling-based clustering method (Monti et al. 2003)

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

Fisher's exact test

For binary clinical features, two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

Download Results

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

References

[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)

[2] Monti et al., Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data, Machine Learning 52(1):91-118 (2003)

[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)

[4] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)

[5] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)

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